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Titlebook: Introduction to Nonsmooth Optimization; Theory, Practice and Adil Bagirov,Napsu Karmitsa,Marko M. Mäkelä Book 2014 Springer International P

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发表于 2025-3-21 19:57:33 | 显示全部楼层 |阅读模式
书目名称Introduction to Nonsmooth Optimization
副标题Theory, Practice and
编辑Adil Bagirov,Napsu Karmitsa,Marko M. Mäkelä
视频video
概述Attempts to be the first easy-to-read book about nonsmooth optimization.Covers both the theory and the numerical methods used in nonsmooth optimization and offers a survey of different problems arisin
图书封面Titlebook: Introduction to Nonsmooth Optimization; Theory, Practice and Adil Bagirov,Napsu Karmitsa,Marko M. Mäkelä Book 2014 Springer International P
描述.This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Solving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, economics and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO and provide an overview of different problems arising in the field. It is organized into three parts:.1. convex and nonconvex analysis and the theory of NSO;.2. test problems and practical applications;.3. a guide to NSO software..The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.
出版日期Book 2014
关键词Bundle methods; Nondifferentiable optimization; Nonsmooth analysis; Subgradient methods; Test problems
版次1
doihttps://doi.org/10.1007/978-3-319-08114-4
isbn_softcover978-3-319-34627-4
isbn_ebook978-3-319-08114-4
copyrightSpringer International Publishing Switzerland 2014
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发表于 2025-3-22 01:01:00 | 显示全部楼层
https://doi.org/10.1007/978-3-319-08114-4Bundle methods; Nondifferentiable optimization; Nonsmooth analysis; Subgradient methods; Test problems
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978-3-319-34627-4Springer International Publishing Switzerland 2014
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发表于 2025-3-22 17:23:38 | 显示全部楼层
Academic Problemsion which can be and have been used to test the nonsmooth optimization software. We use a classification of test problems such that it is easy to pick up those problems that share features in interest. That is, for instance, convexity or nonconvexity, or min-max, piecewise quadratic or general polynomial objective function structure, etc.
发表于 2025-3-23 00:51:05 | 显示全部楼层
Hybrid Methodsods to be introduced are the . and the . for solving general small- and medium-scale nonsmooth optimization problems; the modification of variable metric bundle method for solving large-scale nonsmooth optimization problems, that is, the .; and the . for extremely large-scale convex nonsmooth optimization problems.
发表于 2025-3-23 04:20:13 | 显示全部楼层
Nonconvex Analysis subdifferential, many different generalizations of the concept of a subdifferential for nonconvex nonsmooth functions exist. At the end of this chapter we briefly recall some of them. More specifically we give definitions of the quasidifferential, the codifferential, the basic (limiting) and the singular subdifferentials.
发表于 2025-3-23 06:12:03 | 显示全部楼层
Generalized Convexitieses of the generalized pseudo- and quasiconvexities for nondifferentiable locally Lipschitz continuous functions. The treatment is based on the Clarke subdifferentials and generalized directional derivatives.
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